Image Embedding with Timm

author: Jael Gu, Filip

Description

An image embedding operator generates a vector given an image. This operator extracts features for image with pre-trained models provided by Timm. Timm is a deep-learning library developed by Ross Wightman, who maintains SOTA deep-learning models and tools in computer vision.

Code Example

Load an image from path './towhee.jpeg' and use the pre-trained ResNet50 model ('resnet50') to generate an image embedding.

Write the pipeline in simplified style:

import towhee

towhee.glob('./towhee.jpeg') \
      .image_decode() \
      .image_embedding.timm(model_name='resnet50') \
      .show()

Write a same pipeline with explicit inputs/outputs name specifications:

import towhee

towhee.glob['path']('./towhee.jpeg') \
      .image_decode['path', 'img']() \
      .image_embedding.timm['img', 'vec'](model_name='resnet50') \
      .select['img', 'vec']() \
      .show()

Factory Constructor

Create the operator via the following factory method:

image_embedding.timm(model_name='resnet34', num_classes=1000, skip_preprocess=False)

Parameters:

model_name: str

The model name in string. The default value is "resnet34". Refer to Timm Docs to get a full list of supported models.

num_classes: int

The number of classes. The default value is 1000. It is related to model and dataset.

skip_preprocess: bool

The flag to control whether to skip image pre-process. The default value is False. If set to True, it will skip image preprocessing steps (transforms). In this case, input image data must be prepared in advance in order to properly fit the model.

Interface

An image embedding operator takes a towhee image as input. It uses the pre-trained model specified by model name to generate an image embedding in ndarray.

Parameters:

data: towhee.types.Image

The decoded image data in towhee Image (a subset of numpy.ndarray).

Returns: numpy.ndarray

An image embedding generated by model, in shape of (feature_dim,).

Towhee Serve

Models which is supported the towhee.serve.

Model List

models	models	models
adv_inception_v3	bat_resnext26ts	beit_base_patch16_224
beit_base_patch16_224_in22k	beit_base_patch16_384	beit_large_patch16_224
beit_large_patch16_224_in22k	beit_large_patch16_384	beit_large_patch16_512
botnet26t_256	cait_m36_384	cait_m48_448
cait_s24_224	cait_s24_384	cait_s36_384
cait_xs24_384	cait_xxs24_224	cait_xxs24_384
cait_xxs36_224	cait_xxs36_384	coat_lite_mini
coat_lite_small	coat_lite_tiny	convit_base
convit_small	convit_tiny	convmixer_768_32
convmixer_1024_20_ks9_p14	convmixer_1536_20	convnext_base
convnext_base_384_in22ft1k	convnext_base_in22ft1k	convnext_base_in22k
convnext_large	convnext_large_384_in22ft1k	convnext_large_in22ft1k
convnext_large_in22k	convnext_small	convnext_small_384_in22ft1k
convnext_small_in22ft1k	convnext_small_in22k	convnext_tiny
convnext_tiny_384_in22ft1k	convnext_tiny_hnf	convnext_tiny_in22ft1k
convnext_tiny_in22k	convnext_xlarge_384_in22ft1k	convnext_xlarge_in22ft1k
convnext_xlarge_in22k	cs3darknet_focus_l	cs3darknet_focus_m
cs3darknet_l	cs3darknet_m	cspdarknet53
cspresnet50	cspresnext50	darknet53
deit3_base_patch16_224	deit3_base_patch16_224_in21ft1k	deit3_base_patch16_384
deit3_base_patch16_384_in21ft1k	deit3_huge_patch14_224	deit3_huge_patch14_224_in21ft1k
deit3_large_patch16_224	deit3_large_patch16_224_in21ft1k	deit3_large_patch16_384
deit3_large_patch16_384_in21ft1k	deit3_small_patch16_224	deit3_small_patch16_224_in21ft1k
deit3_small_patch16_384	deit3_small_patch16_384_in21ft1k	deit_base_distilled_patch16_224
deit_base_distilled_patch16_384	deit_base_patch16_224	deit_base_patch16_384
deit_small_distilled_patch16_224	deit_small_patch16_224	deit_tiny_distilled_patch16_224
deit_tiny_patch16_224	densenet121	densenet161
densenet169	densenet201	densenetblur121d
dla34	dla46_c	dla46x_c
dla60	dla60_res2net	dla60_res2next
dla60x	dla60x_c	dla102
dla102x	dla102x2	dla169
dm_nfnet_f0	dm_nfnet_f1	dm_nfnet_f2
dm_nfnet_f3	dm_nfnet_f4	dm_nfnet_f5
dm_nfnet_f6	dpn68	dpn68b
dpn92	dpn98	dpn107
dpn131	eca_botnext26ts_256	eca_halonext26ts
eca_nfnet_l0	eca_nfnet_l1	eca_nfnet_l2
eca_resnet33ts	eca_resnext26ts	ecaresnet26t
ecaresnet50t	ecaresnet269d	edgenext_small
edgenext_small_rw	edgenext_x_small	edgenext_xx_small
efficientnet_b0	efficientnet_b1	efficientnet_b2
efficientnet_b3	efficientnet_b4	efficientnet_el
efficientnet_el_pruned	efficientnet_em	efficientnet_es
efficientnet_es_pruned	efficientnet_lite0	efficientnetv2_rw_m
efficientnetv2_rw_s	efficientnetv2_rw_t	ens_adv_inception_resnet_v2
ese_vovnet19b_dw	ese_vovnet39b	fbnetc_100
fbnetv3_b	fbnetv3_d	fbnetv3_g
gc_efficientnetv2_rw_t	gcresnet33ts	gcresnet50t
gcresnext26ts	gcresnext50ts	gernet_l
gernet_m	gernet_s	ghostnet_100
gluon_inception_v3	gluon_resnet18_v1b	gluon_resnet34_v1b
gluon_resnet50_v1b	gluon_resnet50_v1c	gluon_resnet50_v1d
gluon_resnet50_v1s	gluon_resnet101_v1b	gluon_resnet101_v1c
gluon_resnet101_v1d	gluon_resnet101_v1s	gluon_resnet152_v1b
gluon_resnet152_v1c	gluon_resnet152_v1d	gluon_resnet152_v1s
gluon_resnext50_32x4d	gluon_resnext101_32x4d	gluon_resnext101_64x4d
gluon_senet154	gluon_seresnext50_32x4d	gluon_seresnext101_32x4d
gluon_seresnext101_64x4d	gluon_xception65	gmlp_s16_224
halo2botnet50ts_25